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The report highlights the key areas where AI applications can support decarbonisation and help achieve net-zero.

A new paper published yesterday by techUK and the Centre for AI and Climate shines a light on the potential opportunities from machine learning and artificial intelligence in supporting the decarbonisation of the grid.

The ‘AI for Energy’ report is the first in a series of whitepapers techUK will be working with partners on over the year outlining how emerging technologies can support the net zero transition of the energy sector. Machine learning and AI are being seen crucially as a vital technology to manage the increasing complexity of the energy system as it decentralises and decarbonises.

Yesterday’s paper outlines the most promising emerging AI and machine learning use cases across the energy system, which include:

• Optimising grid management.
• Short- and long-term renewable energy forecasting to support investment, planning and operational decision-making.
• Supporting demand-side incentives and flexibility.
• Optimising existing technologies.
• Supporting the integration of electric vehicles.
• Improving building and home energy management.
• Increasing the energy efficiency of commercial and industrial facilities.
• Strengthening microgrid management through more accurate forecasts of demand and energy production.

Susanne Baker, techUK’s Associate Director for Climate, Environment and Sustainability yesterday said:

“The decentralisation and decarbonisation of the grid means that the complexity of managing the energy system increases exponentially. AI and machine learning is ideal in helping to manage that complexity. But to do that we need to be really clear where the opportunities are so we can focus our innovation efforts.”

Peter Clutton-Brock, Centre for AI and Climate, yesterday said:

“AI and machine learning will not be nice-to-haves in managing and optimising a more complex, renewables-dominant grid but fundamental pre-requisites of zero emission electricity systems. This paper highlights some of the ways that AI can make significant contributions towards the transition to zero emission energy, however, ultimately AI will flow into all decision-making processes associated with electricity systems.”

You can read the full paper here.

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